Robotic ankle exoskeletons can supplement the user’s biological ankle power with mechanical power from the device. These devices have the potential to restore healthy walking mechanics in individuals with mobility challenges or to augment the performance of able-bodied individuals by enabling them to walk farther, run faster or carry heavier loads. We are interested in how to best control these devices and seek to explore the complex physiological interactions between the exoskeleton and the human user.
Originally designed in the Biomechatronics Group at the MIT Media Lab, we use the Dephy ExoBoot exoskeletons, which are commercially available and manufactured by Dephy, Inc. (Maynard, MA). This system was the first autonomous (untethered) robotic ankle exoskeleton to reduce the energy cost of walking below that of walking without the exoskeleton.
With the development of lightweight, autonomous ankle exoskeletons (like the Dephy ExoBoot), we need to design controllers to assist the user. One commonly defined goal of ankle exoskeletons is to reduce the user’s metabolic cost—but this cost function doesn’t take into account more subjective measures such as pain, comfort, stability, or satisfaction. One way to tease out these subjective elements and how they relate to different control strategies is through user feedback about their preference. We let users self-tune their exoskeleton actuation profiles in 2 dimensions by controlling both the timing and magnitude of peak torque delivery using a touch screen tablet. We are interested in how reliably subjects can tune their own exoskeleton actuation settings in 2 dimensions and arrive at their preferred setting.
We are also investigating basic physiological questions, like evaluating how well users can sense differences in exoskeleton controllers and how well they can sense their own metabolic effort. To characterize this ability, we are calculating the Just Noticeable Difference (JND) of metabolic cost, which is the minimum perceivable change in metabolic cost that can be reliably detected. We use the ExoBoot to repeatedly impose different metabolic costs on test subjects over two-minute intervals. We then measure change in metabolic cost during each interval and ask subjects if they think the current cost is higher than previous one. We then aggregate these responses and obtain each subject’s JND.
Contributors: Leo Medrano, Kim Ingraham, Elliott Rouse
Medrano, R., Thomas, G. C., & Rouse, E. J. (2020). Methods for Measuring the Just Noticeable Difference for Variable Stimuli: Implications for Perception of Metabolic Rate with Exoskeleton Assistance. In International Conference on Biomedical Robotics and Biomechatronics. doi.org/10.1109/BioRob49111.2020.9224374
Mooney, L. M., Rouse, E. J., & Herr, H. M. (2014). Autonomous exoskeleton reduces metabolic cost of human walking. Journal of NeuroEngineering and Rehabilitation, 11(1), 151. doi.org/10.1186/1743-0003-11-151
Mooney, L. M., Rouse, E. J., & Herr, H. M. (2014). Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. Journal of NeuroEngineering and Rehabilitation, 11(1), 80. doi.org/10.1186/1743-0003-11-80